AI Documentation for Regulatory Compliance: 10 Key Requirements

Here's a quick overview of the 10 key requirements for AI documentation to ensure regulatory compliance:

1. System Overview
2. Data Management
3. Model Development
4. Risk Assessment
5. Transparency and Explainability
6. Human Oversight
7. Security Measures
8. Testing and Validation
9. Version Control and Change Management
10. Compliance Monitoring and Reporting

These requirements help companies:

• Follow AI regulations
• Build trust in AI systems
• Reduce risks
• Prepare for future rule changes

Requirement	Purpose	Key Components
System Overview	Explain AI system basics	Purpose, version, interactions
Data Management	Track data use	Data sources, rules, usage reasons
Model Development	Document AI creation	Design, build process, data needs
Risk Assessment	Identify potential issues	Safety, privacy, fairness checks
Transparency	Make AI understandable	Explanation methods, clarity level
Human Oversight	Ensure human control	Oversight roles, intervention processes
Security Measures	Protect AI systems	Access control, data protection, monitoring
Testing	Verify AI performance	Test methods, datasets, results
Version Control	Track AI changes	Change logs, version numbers, impact assessments
Compliance Monitoring	Ensure ongoing rule-following	Audit schedules, reporting processes

Good AI documentation is crucial as regulations become stricter. Companies that document well now will be ready for future changes.

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1. System Overview

Documentation Details

The system overview is a key part of AI documentation for following rules. It gives a general description of the AI system, including:

• Purpose and version
• How it works with hardware or software
• Software or firmware versions
• How it's sold or used
• What hardware it needs
• Outside features and inside layout (for built-in systems)
• How users interact with it

This overview helps people understand what the AI system does and how it works.

Why It's Important for Rules

Writing down the system overview is needed to follow AI rules, like the EU AI Act. It shows openness and gives rule-makers the info they need to check if the AI system follows the rules. For AI systems with high risks, this must be done before selling or using the system, and kept up to date.

How to Do It

To write a good system overview:

1. Use clear, short descriptions
2. Add pictures or drawings where helpful
3. Make sure everyone can understand the information
4. Update the document when the system changes

What to Include	What It Means
Purpose	What the AI system is for
Version	Current version and how it relates to older ones
How It Works with Other Things	How the system works with other hardware/software
How It's Used	All the ways the system is sold or used (e.g., software, APIs)
Hardware Needs	What hardware is needed to run the AI system
User Interface	Basic description of how people use the system

2. Data Management

Documentation Details

Data management is a key part of AI documentation for following rules. It includes:

• Data history: Tracking where data comes from and how it changes in AI systems.
• Data rules: How data is gathered, studied, kept, and used in AI projects.
• Data use reasons: Writing down why the AI system needs to use data.

Why It Matters for Rules

Good data management helps follow many rules, such as:

• GDPR: Makes data use clear and protects people's data rights.
• EU AI Act: Needs records of data practices for risky AI systems.
• Other rules: Like HIPAA for health data and PCI DSS for money data.

How to Do It

To manage data well for AI rules:

1. Make clear data rules for AI work.
2. Keep track of data changes to show where it comes from.
3. Check AI systems often to make sure they follow data privacy rules.
4. Let people see, delete, or ask for human checks of their data.
5. Use tools to help track data and manage user rights.

What to Do	How to Do It
Track Data History	Use tools that follow data changes
Make Data Rules	Write rules just for AI data use
Write Down Data Use Reasons	Explain clearly why data is needed
Check for Rule-Following	Set up regular checks
Help with User Data Rights	Make ways to handle user requests

3. Model Development

Documentation Details

Model development documentation is important for following AI rules. It should include:

• How it's made: Steps used to create the AI system, including any ready-made systems or outside tools used.
• How it's designed: Explanation of the AI system's basic logic, math, and main design choices. This includes reasons for decisions and how it's meant to be used.
• How it's built: Description of how software parts work together and what computer resources are used for making, training, and testing the system.
• What data it needs: Info on training methods and datasets used, including where they come from and what they cover.

Why It's Important for Rules

Good model development documentation helps:

• Make the AI system clear to others
• Follow AI rules like the EU AI Act
• Make checks and inspections easier
• Show the AI was made in a good way

How to Do It

To make sure model development follows the rules:

1. Keep a record of all development work and decisions.
2. Write down how the system is designed, including its logic and main settings.
3. Keep detailed records of data sources and training methods.
4. Use version control for all model changes.
5. Check and update documentation regularly when the AI system changes.

What to Document	What to Include
Making Process	Steps, outside tools used
Design Details	Basic logic, math, main choices
System Structure	How parts work together, computer needs
Data Needs	Training data, methods, main features

4. Risk Assessment

Documentation Details

Risk assessment documentation for AI systems should include:

• How serious each risk could be
• How likely risks are to happen
• A list of all AI risks
• Checks on safety, privacy, fairness, and responsibility

Why It's Important for Rules

Good risk assessment documentation helps:

• Show that AI follows rules in different places
• Meet standards set by groups like ISO, NIST, and FTC
• Deal with possible legal and ethical issues
• Build trust with the public

How to Do It

To check AI risks well:

1. Make clear rules for building and writing about AI
2. Have outside experts look at AI systems
3. Check risks about private data and possible data leaks
4. Look at how AI might affect important business work
5. Think about if AI could cause physical harm

Risk Type	What to Check
Private Data	How sensitive the data is, what rules apply (like PCI, HIPAA, GLBA)
Business Effects	How important it is for work, what bad things could happen
Safety	Could it hurt people or cause deaths
Important Systems	How it affects key things (like water, power, health)

Keep checking risks as AI changes and new rules come out. This helps make sure AI stays safe and follows the rules.

5. Transparency and Explainability

Clear AI systems help people understand how they work. This is important for following rules.

What to Write Down

AI papers should include:

• How clear the AI is (hard to understand, partly clear, or easy to see)
• Ways to explain the AI (pictures, important parts, simple words)
• "What if" examples
• How the AI works and what info it uses

Why It Matters for Rules

Many rules say AI needs to be clear:

• GDPR rules
• OECD AI rules
• UK 2023 AI paper
• EU AI Act (for risky AI)

These rules want to make sure people can understand AI choices.

How to Do It

To make AI clear and easy to explain:

1. Decide how to explain it before making it
2. Write down how it acts while making it
3. Use ways to explain AI that people can understand
4. Keep checking the AI to make sure it's clear
5. Write simple words to explain AI choices

How Clear It Is	What It Means	When to Use It
Hard to See Inside	Can't see how it works	Big, complex AI
Partly Clear	Can see some parts	Normal AI learning
Easy to See Inside	Can see all parts	Simple rule AI

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6. Human Oversight

Human oversight makes sure people stay in control of AI systems and that these systems follow rules and ethics.

What to Write Down

When writing about human oversight for AI, include:

• Who watches over the AI and what they do
• How people can step in and stop the AI if needed
• What training people need to watch AI
• How to spot and fix strange AI behavior
• Ways to avoid trusting AI too much

Why It's Important for Rules

Many rules say AI needs human oversight:

• EU AI Act: Says high-risk AI must have human watchers
• GDPR: Says people must be able to step in for AI choices
• UK's AI Plan: Says people should work with AI

How to Do It

To set up good human oversight:

1. Make AI easy for people to use and control
2. Write clear steps for when people should step in
3. Train the people who will watch the AI
4. Set up ways to spot problems and tell people
5. Check often to make sure oversight is working well

How People Watch AI	What It Means	When to Use It
People Help Every Time	People step in for each AI choice	For very important choices
People Watch Closely	People keep an eye on AI as it works	For ongoing tasks
People in Charge	People oversee all AI work	For big-picture control

7. Security Measures

Security measures protect AI systems from threats. Good security keeps AI applications and data safe.

What to Write Down

When writing about AI security, include:

• Security frameworks used (e.g., OWASP Top 10 LLM Security Risks, Google's SAIF)
• How access is controlled and data is protected
• How inputs are checked and handled
• How AI operations are watched and recorded
• Plans for dealing with AI security problems

Why It's Important for Rules

Many rules say AI needs good security:

• EU AI Act: Wants good cybersecurity measures
• GDPR: Says personal data in AI must be protected
• NIST AI Risk Management Framework: Gives tips for managing AI security risks

How to Do It

To set up good security for AI systems:

1. Use a security framework made for AI, like Google's SAIF or NIST's AI Risk Management Framework.
2. Control who can access the system:
   • Use special access for backend systems and model storage
   • Make sure users prove who they are and what they can do
3. Protect data better:
   • Keep different data separate
   • Use codes to protect data when it's stored or sent
4. Check inputs carefully:
   • Look for threats in input data
   • Check prompts and inputs to stop attacks
5. Watch and record system activity:
   • Keep an eye on how the AI system behaves
   • Look for strange behavior
6. Make a plan for AI security problems:
   • Write down what to do if there's a security breach
   • Practice the plan regularly
7. Teach employees about security:
   • Show staff the risks and best practices for AI security
   • Keep teaching about new threats

Security Step	What It Does	How to Do It
Control Access	Stops people who shouldn't use the system	Use special access rules, make users prove who they are
Protect Data	Keeps important information safe	Use codes for data, keep different data separate
Check Inputs	Stops attacks through user inputs	Clean up user inputs, check prompts
Watch the System	Finds odd behavior and threats	Always watch the system, use AI tools to help
Plan for Problems	Helps deal with security issues	Write down what to do, practice the plan

8. Testing and Validation

Testing and validation are key parts of AI system documentation for following rules. These steps make sure AI models work well, stay accurate, and follow set standards.

What to Write Down

When writing about testing and validation for AI systems, include:

• How testing is done
• Ways to check the system (like using separate data sets)
• What test data is used and what it's like
• Test sets for checking the whole system
• Results from thorough AI system checks
• Steps taken to stop the system from learning test data too well

Why It's Important for Rules

Good testing and validation records help meet rules in different places. They show:

• The system follows standards for fairness, safety, and openness
• It follows rules for specific industries
• The company wants to keep AI models working well

How to Do It

To test and check AI systems well:

1. Make test data sets that cover many situations
2. Use methods to check the system in different ways
3. Do lots of tests to make sure the system is fair, safe, and open
4. Get help from experts to follow AI testing rules
5. Keep checking systems that are already being used

Checking Method	What It Is	Why It's Good
Separate Test Data	Keep some data just for testing	Gives a fair test, stops over-learning
Multiple Checks	Use different parts of data for training and testing	Uses all data well, gives a better test
Full System Tests	Big tests that cover many situations	Makes sure the whole AI system works well
Keep Checking	Always watch and test systems being used	Keeps the system working well over time

9. Version Control and Change Management

Version control and change management help keep track of AI system changes and follow rules.

What to Write Down

When writing about version control and change management for AI systems:

• List all changes to the AI system, including updates to algorithms, models, and data sets
• Keep a detailed list of changes with dates, who made them, and what was changed
• Use version numbers for each update of the AI system
• Write down why changes were made (e.g., fixing bugs, making it work better, following new rules)
• Note how changes affect how well the system works and if it still follows rules

Why It's Important for Rules

Good version control and change management records help:

• Show the AI system follows changing rules
• Make it easier for rule-makers to check the system
• Find and fix problems with specific versions quickly
• Show how AI is made and used clearly

How to Do It

To keep track of versions and changes well:

1. Use tools like Git or Apache Subversion
2. Make clear steps for how to update the system
3. Check how changes affect the AI system's work and rule-following
4. Teach team members how to write down changes correctly
5. Make version control part of how you manage AI overall

What to Do	How to Do It	Why It Helps
Use version control tools	Set up Git, SVN, or similar systems	Keeps track of changes, helps people work together
Write detailed change lists	Write down all changes with extra info	Shows who did what and when
Check how changes affect things	Look at how changes impact work and rule-following	Lowers risks and keeps following rules
Check records often	Look over version history and change records	Finds possible problems and keeps records good

10. Compliance Monitoring and Reporting

Compliance monitoring and reporting help make sure AI systems follow rules and laws.

What to Write Down

When writing about how to check and report on AI rule-following:

• How often the AI system is checked
• Ways to find and report when rules aren't followed
• How often big checks (audits) happen and what they look at
• What numbers are used to measure rule-following
• How reports are made and shared

Why It's Important for Rules

Good checking and reporting helps companies:

• Show they follow AI rules and industry standards
• Find and fix possible rule-breaking before it's a problem
• Be open with rule-makers and others who care
• Make people trust AI systems by watching them closely

How to Do It

To check and report on rule-following well:

1. Use AI to help watch:
   • Let AI look at lots of information
   • Set up systems that watch all the time and tell you about problems
2. Set clear goals:
   • Choose what you want to achieve with rule-following
   • Check these goals often as rules change
3. Make sure information is correct:
   • Have good ways to manage information
   • Check and update the information used to watch rule-following
4. Get different teams to work together:
   • Make sure people who know about rules talk to people who know about computers
   • Keep these teams talking to each other
5. Keep making AI better:
   • Check how well the AI system is working
   • Change the AI when there's new information or new rules

What to Do	How It Helps
Use AI to watch	Finds problems faster and better
Set clear goals	Knows what to look for
Use good information	Makes sure checks are right
Teams work together	AI and rules work well together
Keep improving AI	Keeps up with new rules

Conclusion

As AI keeps growing, following rules has become very important for making and using AI responsibly. Companies need to write down everything about their AI systems to make sure they follow the rules in different places and industries.

Here are the main things we learned about writing down AI information for following rules:

1. Cover everything: Write about all parts of AI, from how it works to how it's checked.
2. Be open: Clear records help people trust AI and show it's being used the right way.
3. Lower risks: Good records help find and fix problems with AI systems, which protects companies.
4. Change when needed: As rules change, the way companies keep records needs to change too.

In the future, we think AI rules will get stricter. Companies that start keeping good records now will be ready for these changes and can use AI better.

What might happen	How it affects record-keeping
Stricter rules	Need to write down more details
Focus on explaining AI	Need to show clearly how AI makes choices
Rules in different countries	Need records that work for rules in many places
Care about AI being good	Need to write about how AI affects people and if it's fair

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